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RESEARCH Open Access
Network analysis of skin tumor progression
identifies a rewired genetic architecture affecting
inflammation and tumor susceptibility
David A Quigley
1
, Minh D To
1,2
, Il Jin Kim
1,2
, Kevin K Lin
1
, Donna G Albertson
1,3
, Jonas Sjolund
1
,
Jesús Pérez-Losada
4
, Allan Balmain
1*
Abstract
Background: Germline polymorphisms can influence gene expression networks in normal mammalian tissues and
can affect disease susceptibility. We and others have shown that analysis of this genetic architecture can identify
single genes and whole pathways that influence complex traits, including inflammation and cancer susceptibility.
Whether germline variants affect gene expression in tumors that have undergone somatic alterations, and the
extent to which these variants influence tumor progression, is unknown.
Results: Using an integrated linkage and genomic analysis of a mouse model of skin cancer that produces both
benign tumors and malignant carcinomas, we docume nt major changes in germline control of gene expres sion
during skin tumor development resulting from cell selection, somatic genetic events, and changes in the tumor
microenvironment. The number of significant expression quantitative trait loci (eQTL) is progressively reduced in

benign and malignant skin tumors when compared to normal skin. However, novel tumor-specific eQTL are
detected for several genes associated with tumor susceptibility, including IL18 (Il18), Granzyme E (Gzme), Sprouty
homolog 2 (Spry2), and Mitogen-activated protein kinase kinase 4 (Map2k4).
Conclusions: We conclude that the genetic architecture is substantially altered in tumors, and that eQT L analysis
of tumors can identify host factors that influence the tumor microenvironment, mitogen-activated protein (MAP)
kinase signaling, and cancer susceptibility.
Background
Common genetic variants have been shown to affect
many complex traits, including cancer susceptibility [1].
However, factors responsible for most of the expected
heritable risk of cancer development have not yet been
identified. Finding these alleles and isolating the causal
polymorphisms is challenging because the heritable com-
ponent of susceptibility is influenced by many alleles
exerting modest effects that may be pleiotropic, epistatic,
or context-dependent [2,3]. Mouse models of cancer
using inbred strains of a defined genetic background do
not recapitulate the genetic heterogeneity inherent in
human populations. However, genetically heterogeneous
mouse crosses permit analysis of the combinatorial
effects of host genetic background and somatic events
during tumo r evolution, a nd these cros ses have been
used to identify polymorphisms that influence tumor
susceptibility and progression [4-7]. Analysis of the
geneticarchitectureofgeneexpressioninnormalskin
from a Mus spretus/Mus musculus backcross ([SPRET/Ei
X FVB/N] X FVB/N, hereafter FVBBX) identified expres-
sion quantitative trait loci (eQTL) that influence both
structural and functional phe notypes, including hair folli-
cle development, inflammation and tumor susceptibility

[8]. A systematic analysis of germline influence on gene
expression in benign and malignant skin tumors could
identify novel alleles that influence tumorigenesis but
areundetectablebyanalysisofnormaltissue.Herewe
demonstrate that somatic alterations during tumor
progression reduce the detectable influence of g ermline
polymorphisms,butallelesthatarenotrelevantin
normal tissue are found to influence innate immune
* Correspondence:
1
Helen Diller Family Comprehensive Cancer Center, University of California
San Francisco, 1450 Third St, San Francisco, CA 94158, USA
Full list of author information is available at the end of the article
Quigley et al. Genome Biology 2011, 12:R5
/>© 2011 Quigley et al.; licensee BioMed Central Ltd. This is an open access article distribu ted under the terms of the Creative Common s
Attribution License ( licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
responses to skin tumors and are associated with tumor
susceptibility.
Results
Germline control of gene expression is altered in tumors
Skin tumors were induced on a cohort of 71 FVBBX mice
by treatment of dorsal back skin with dimethyl be nzan-
thracene (DMBA) and tetradecanoyl-phorbol acetate
(TPA) (see experimental design in Figure S1 of Additional
file 1). This treatment induced multiple benign papillomas
as well as malignant carcinomas. Gene expression analysis
was performed on mRNA extracted from 68 of these
papillomas : two papillomas from each of 31 FVBBX mice
and a single papilloma from six additional FVBBX mice.

Gene expression and DNA copy number analysis was per-
formed on 60 carcinomas that developed on these animals.
A second cohort of 28 FVBBX animals (the ‘confirmation’
cohort) was subsequently generated and treated with the
same carcinogenesis protocolasthefirstsetofmicein
order to confirm gene expression and eQTL results from
the discovery cohort.
Germline polymorphisms have been shown to influ-
ence gene expression in tissues from model organisms
and humans [8-13], but it is not clear how this influence
is altered during tumor progression. If the germline
plays no significant role in tumor gene expression, we
would expect papilloma gene expression profiles from
the same host to cluster near each other only by chance.
Hierarchical clustering of gene expression profiles from
papillomas demonstrated that tumors from the same
mouse are most similar to each other in 19 of the 31
papilloma pairs (Figure 1a). The highly significant simi-
larity of gene expression from same-host papillomas
suggested that germline polymorphisms affect constitu-
tive levels of gene expression in benign tumors (P <
0.00001 by permutation; see Materials and methods).
The contribution of genetic background to the benign
and malignant tumor gene expression profiles was quan-
tified by eQTL analysis. Our previous study of normal
skin from the same animals identified almost 8,000
candidate eQTL at ≤10% false discovery rate (FDR).
We identified 3,408 candidate eQTL in the 68 papillo-
mas and 912 candidate eQTL in the 60 carcinomas sig-
nificant at ≤10% FDR (Figure 1b; carcinoma eQTL listed

in Table S1 in Additional file 1). At ≤5% FDR we identi-
fied 2,175 and 674 candidate eQTL in papillomas and
carcinomas, respectively; increasing statistical stringency
reduced the number of candidate eQTL but did not
change the subsequent results qua litatively, and we
report eQTL significant at the 10% FDR level.
The striking reduction in eQTL detected in tumors,
particularly in malignant carcinomas, prompted us to
investigate reasons why fewer genes are significantly
influenced by germline polymorphisms in carcinomas
than in normal skin. Of the 7,414 genes with significant
eQTL in skin, only 237 are not expressed in tumors, so
complete absence of gene expression explains only
about 3% of the decrease. EQTLs affecting genes that
did not undergo drastic changes (mor e than two stan-
dard deviations from the mean fold-change) in their
expression levels were more likely to be conserved
between skin and carcinomas (P < 7.4e-06, Fisher exact
test). Conserved eQTL had significantly stronger statisti-
cal significance in normal skin than non-conserved
eQTL (P < 1e-16, Wilcoxon signed rank test). In normal
skin we identified eQTL acting in cis (where the locus i s
physically proximal to the gene it affects) and in trans
(where the locus is distant from or on another chromo-
some from the gene it affects) with approximately equal
frequencies. The most statistically significant eQTL in
skin acted overwhelmingly in cis.Thecis/trans propor-
tion detected in tails was 0.8/1, whi le in papillomas it
was approximately 1.5/1, and in carcinomas it was
approximately 5.75/1 ( exact counts are listed in Table

S2inAdditionalfile1).Weconcludethatonlyvery
strong eQTL effects carry through from normal skin to
affect the malignan t carcinomas, and weaker trans-
acting effects are rarely conserved.
Somatic events alter the genetic architecture of gene
expression in tumors
Changes in the wiring of signaling pathways through
epigenetic or genetic alterations may alter the influence
of germline polymorphisms on gene expression in trans-
formed cells. We used array comparative genomic hybri-
dization (aCGH) analysis to quantify alterations in
tumor DNA copy number. Tumors showed widespread
genomic instability (Figure 2a). The most frequent target
of large-scale amplification in FVBBX carcinomas was
distal chromosome 7, which showed copy number gains
in 45% (27 of 60) of carcinomas. Chromosome seven
had a markedly smaller p ercentage of eQTL conserved
between skin and carcinomas (2.2%) than other autoso-
mal chromosomes (mean 10%, range 2.2% to -15%;
Figure 2b). We identified a significant correlation
between amplification of the most distal probe on chro-
mosome 7 and fold-change increases of several genes
located near the probe, including Ccnd1 (encoding
Cyclin D1; P = 3.0e-6, mean 10.5-fold up-regulation;
Figure 2c). Cyclin D1 amplification or overexpression is
an early event in numerous human tumors, and targeted
over-expression of Ccnd1 drives several mouse models
of carcinogenesis [14-16]. Although Ccnd1 had a signifi-
cant cis-eQTL in skin (uncorrected P = 0.0001, permu-
tation P=0.009, q < 0.015), this cis-eQTL was not

detected in papillomas or carcinomas.
DMBA induces a characteristic activ ating mutation in
Hras1 [17], which is also located on distal chromos ome
Quigley et al. Genome Biology 2011, 12:R5
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7 in the mouse. Hras1 also had a significant cis-eQTL in
skin (uncorrected P = 8.7e-5, permutation P = 0.013, q <
0.02) that was not detected in papillomas or carcinomas.
Changes in Hras1 mutant gene copy number and/or
loss of the normal wild-type allele play a role in tumor
progression, and trisomy of chromosome 7 is a common
early event in both papillomas and carcinomas, leading
to increased copy number of the mutant Hras1 allele
[18,19]. We conclude that gene copy number alterations
on distal chromosome 7 have disrupted the normal
genetic control of expression of these target genes.
Genomic networks are rewired during tumorigenesis
Changes in gene expression networks in tumors can
result from ma croscopic alterati ons in ce llular composi-
tion during transformation, or from rewiring of signaling
pathways. Coordinated alterations in gene expression
from normal to tumor can be visualized as a ‘ progres-
sion network’ by combining correlation and differential
expression analysis (see Materials and methods; genes
used to build this network and fold-change values are
listed in Table S3 in Additional file 1). This method
identifies functionally related gene sets with significantly
(
a
)

eQTL Count
Total cis trans
(b)
Skin
Papillomas
Carcinomas
8000
7000
6000
5000
4000
3000
2000
1000
0
Figure 1 The influence of germline polymorphisms on gene expression is present but reduced in tumors. (a) Hierarchical clustering of
total gene expression from papilloma pairs indicates that germline polymorphisms continue to exert a major effect on gene expression at the
benign tumor stage. Bars indicate when both papillomas in a pair are most similar to each other. (b) Counts of total, cis-, and trans- eQTL in
skin, papillomas, and carcinomas, showing that overall germline control of gene expression is strongly reduced, particularly for trans-eQTL, in
malignant carcinomas.
Quigley et al. Genome Biology 2011, 12:R5
/>Page 3 of 11
correlated changes in expression between two states.
The global network construc ted in this way is shown in
Figure 3 and demonstrates t hat pathways linked to
mitosis, stress responses, and IL1-mediated signaling are
seen as distinct network motifs that are up-regulated in
carcinomas. Carcinomas result from cl onal expansion of
initiated epidermal cells, and this is reflected in the
down-regulation of motifs related to epithelial barrier,

striated muscle, and hair follicles.
We previously identified a hair follicle network in nor-
mal skin genetically linked to the G-protein coupled
receptor gene Lgr5, known to mark hair follicle stem
cells [8,20]. Papillomas do not produce hair follicles,
although they continue to express hair follicle keratins
(Figure 4a; Figure S2 in Additional file 1). Although Lg r5
is significantly expressed in papillomas and carcinomas,
it is not under the control of a cis-eQTL in tumors, and
also is not linked genetically to the hair follicle correla-
tion network. A papillo ma-specific eQTL network
including hair folli cle keratins and ke ratin-associated
proteins was detected with a shared locus of control on
distal chromosome six (Figure 4b), a locus that was not
significantly associated with these genes in normal tissue.
The G-protein coupled receptor family member Gprc5d
was the only cis-eQTL in the new network (raw P =
5.4e-4, permutation P =0.02,q = 0.02; linkage map
plotted in Figure S3 in Additional file 1). Intriguingly,
overexpression of Gprc5d promotes hair keratin gene
expression, and Gprc5d is expressed in whn (hairless)
nude mice [21], compatible with a role that would only
be revealed when normal hair follicle control has been
disrupted. These data suggest that the hair follicle stem
aCGH log
2
ratio
Ccnd1 fold-change
(
a

)
(b) (c)
eQTL count in skin
Chromosome
percent conserved
Percent genome altered
Figure 2 DNA copy number changes reduce germline influence. (a) Percentage of carcinomas with alterations across the mouse genome;
amplifications (blue) plotted above zero, deletions (red) below zero. Chromosome 7 is most frequently amplified. (b) Counts of eQTL in skin on
autosomal chromosomes (grey bars) compared to percentage of those eQTL conserved in carcinomas (black bars). Left-side scale indicates eQTL
counts, right-side scale indicates conservation percentage. Conservation percentage is lowest on chromosome 7. (c) Amplification of aCGH
probe MouseArray1M2_K17, at chromosome 7, 144.5 Mb, is significantly associated with increased expression of Cyclin D1 in carcinomas
compared to matched normal skin. Amplification of this region of distal chromosome 7 accounts for loss of eQTL for Cyclin D1 and other genes
in this region.
Quigley et al. Genome Biology 2011, 12:R5
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cell network is significantly rewired during skin tumor
development, but the possible role of Lgr5 as a marker of
tumor initiating cells remains to be determined. We con-
cludethatgenecopynumberchanges,somaticmuta-
tions, and alterations in tissue composition in pa pillomas
and carcinomas account for the loss of the Ccnd1, Hras1,
and Lgr5 eQTL and likely are responsible f or the loss of
many other eQTL seen in normal skin.
Tumor-specific eQTL are associated with susceptibility
Of the 912 transcripts with significant eQTL in carci-
noma, 210 did not have a significant eQTL in n ormal
skin (carcinoma eQTL are listed in Table S1 in Addi-
tional file 1). Of the 210 eQTL detec ted only in carcino-
mas, in 45 cases the transcript was expressed only in
carcinomas and not in normal tissue. This may be due

to activation of signaling pathways not expressed in
normal skin, or by infiltration of transformed epithelium
by cell populations from the microenvironment not nor-
mally resident in the skin, particularly cells of the innate
and adaptive immune systems. Loci that affect the
expression of transcripts in tumors but not normal skin
may affect tumor susceptibility, but these eQTL would
not be evident from analysis of normal tissue. To iden-
tify genes with tumor-specific eQTL that were asso-
ciated with susce ptibility, we identified genes that were
significantly differentially expressed when contrasting
papillomas from resistant and susceptible animals
(FDR ≤5%). Genes were considered of interest if they
had expression in papillomas significantly associated
with susceptibility and also had a tumor-specific eQTL.
Twenty-nine genes met these criteria (listed in Table 1).
Of these genes, the serine protease Granzyme E (Gzme)
showed the largest induction in papillomas from
Hair
f
ollicle
Muscle
IL-1β
Mitosis
Epithelial barrier
Proli
f
erating epithelium
Stress response
Lipid synthesis

Figure 3 The progression network for squamous cell carcinomas. Gene pairs with significantly correlated expression change and change in
expression level >2 standard deviations from mean between skin and carcinomas are drawn as nodes. Red nodes indicate increased expression
in carcinomas and green nodes indicate decreased expression, with darker color indicating more extreme change. Grey lines connect genes with
significant directly correlated change and blue lines indicate significant inverse correlation. The network demonstrates coordinated increases in
gene expression motifs associated with mitosis, stress response, epidermal lineage proliferation, and IL1-mediated inflammatory responses.
Concomitant decreases are seen in motifs linked to epithelial cell barrier function, hair follicles, lipid biosynthesis, and muscle cells due to major
alterations in cell populations in carcinomas compared to normal skin.
Quigley et al. Genome Biology 2011, 12:R5
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resist ant mice. Gzme is ex pressed in granules released by
cytotoxic T lymphocytes and together with perforin can
destroy pathogen-infected or transformed cells [22,23].
Gzme was expressed at background levels in normal
FVBBX skin, but at a range of detectable levels in papillo-
mas and carcinomas (Figure 5a). The tumor-specific cis-
eQTL for Gzme peaked at chromosome 14, 51 Mb in
papillomas and carcinomas (raw P = 6.6e-7, permutation
P < 0.001, q < 0.001; Figure 5b). Mice heterozygous at the
eQTL locus (that is, with Gzme alleles inherited from both
FVB/N and SPRET/Ei) had higher expression of Gzme in
papillomas and carcinomas than mice homozygous for
FVB/N at this allele. Although (as previously reported [8])
classical QTL analysis of papilloma counts for these
FVBBX mice did not identify a locus significant after mul-
tiple test correction, the strongest linkage was to markers
on chromosome 14, peaking at 62 Mb (linkage map
plotted in Figure S4 in Additional file 1). The SPRET/Ei
allele was protective at this locus, in agreement with the
direction of the Gzme eQTL and susceptibility results. We
conclude that Gzme is a strong candidate modifier of

papilloma susceptibility based on genetic control of gene
expression in tumor tissue, higher levels of expression in
papillomas from resistant mice carrying t he SPRET/Ei
allele, and the documented biological activity of granzymes
in killing of potential tumor cells.
Higher expression of several other genes was asso-
ciated with resistance, including Sprouty homolog two
( Spry2), a negative regulator of Ras/mitogen-activated
protein kinase (MAPK) signaling (raw P =7.6e-8,per-
mutation P < 0.01, q = 0.03). Spry2 was also expressed
at very low levels in normal skin, but wa s expressed at
elevated levels in tumors. The DMBA/TPA model o f
carcinogenesis is driven by oncogenic signaling through
the R as pathway, and it is plausible that mice with
higher constitutive levels of Spry2 expression in tumors
would show greater resistance to tumorigenesis.
Some genes associated with susceptibility are
expressed in normal skin but are only under germline
control in tumors. The IL1 family member IL18 (Il18)
was expressed in skin and tumor samples, but only in
carcinomas did Il18 have a strong cis-eQTL, with higher
expression in papillomas from susceptible animals and
when a SPRET/Ei allele was present (raw P =2.6e-8,
permutation P < 0.001, q = 0.001). Higher levels of the
kinase Map2k4 (also called Mek4 or Mkk4)arealso
ass ociated with increased susceptibility, and this gene is
under germline control only in tumors (raw P =3.5e-5,
permutation P =0.005,q = 0.014). A recent report has
shown that FVB mice with a skin-specific knockout of
Map2k4 are resistant to the DMBA/TPA tumorigenesis

protocol, consistent with our eQTL analysis [24].
Perturbation from normal expression is controlled
primarily in trans
The tumor eQTL analysis described above was based on
steady state levels of all transcripts detected in tumors.
The availability of m atched normal skin and tumor
tissue enabled us to ask whether the degree of perturba-
tion of transcript levels in the tumors, as opposed to
their steady state levels, is under germline control. We
performed an eQTL analysis on the gene expression
changes when comparing the same probe in matched
normal skin and carcinomas. Including only probes that
were expressed above background in both skin and car-
cinomas and that did not have a significant eQTL in tail
or carcinoma analysis based on steady-state levels, we
identified 55 significant eQTL. In contrast to carcinoma
log
2
expression
PapillomasSkin Carcinomas
(a) (b)
Figure 4 Re-wiring of the Lgr5 hair follicle eQTL network. (a) Gene expression levels of Krt71, Krt25, Msx2,andLgr5 in skin, papillomas, and
carcinomas, showing that while Lgr5 is significantly correlated with Msx2 and Krt71 in normal skin, this association is lost during tumor
progression. (b) A new eQTL network for hair follicle keratins in papillomas where the locus affects Gprc5d (yellow node) in cis and other genes
(blue nodes) in trans. Green lines indicate significant influence of eQTL locus on all genes in the network (≤10% FDR); grey lines indicate
significant gene-gene correlation.
Quigley et al. Genome Biology 2011, 12:R5
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Table 1 Genes with novel eQTL in tumors that are also associated with susceptibility
Symbol Probe Chr. Mb Fold change SAM q-value Higher in Higher genotype eQTL chr. eQTL Mb

Gzme 1421227_at 14 56.7 -16.67 <0.01 Resist. Het. 14 41
Gzme 1450171_x_at 14 56.7 -7.69 <0.01 Resist. Het. 14 41
Mnda 1452349_x_at 1 175.8 -2.94 4.31 Resist. Het. 1 169
2310005E10Rik 1453173_at 6 34.3 -2.27 3.02 Resist. Het. 6 32
Ddx6 1439122_at 9 44.4 -1.82 4.31 Resist. Hom. 9 34
Spry2 1421656_at 14 106.3 -1.39 3.02 Resist. Het. 14 94
Kctd3 1436811_at 1 190.8 1.16 4.31 Susc. Hom. 1 187
Map2k4 1451982_at 11 65.5 1.16 1.38 Susc. Hom. 11 101
Ssr1 1441327_a_at 13 38.1 1.18 3.02 Susc. Hom. 10 106
Ndst2 1417931_at 14 21.5 1.21 3.02 Susc. Hom. 14 19
Ppih 1429832_at 4 119.0 1.23 2.02 Susc. Hom. 5 44
1810063B07Rik 1427905_at 14 20.9 1.23 1.38 Susc. Hom. 14 23
Psme3 1418078_at 11 101.2 1.25 3.02 Susc. Hom. 4 75
Tardbp 1436318_at 4 148.0 1.25 2.02 Susc. Hom. 1 187
BC003266 1449189_at 4 126.9 1.25 <0.01 Susc. Hom. 4 121
Acbd6 1452601_a_at 1 157.4 1.26 2.02 Susc. Het. 9 116
2810457I06Rik 1436805_at 9 40.8 1.26 0.93 Susc. Het. 9 34
Dhdds 1450654_a_at 4 133.5 1.26 <0.01 Susc. Hom. 4 141
Nrd1 1424391_at 4 108.7 1.28 0.93 Susc. Hom. 10 118
Nme6 1448574_at 9 109.7 1.3 0.93 Susc. Het. 9 102
Sept8 1426802_at 11 53.3 1.35 2.02 Susc. Hom. 4 106
Hyls1 1431315_at 9 35.4 1.38 1.38 Susc. Het. 9 34
Pus3 1418491_a_at 9 35.4 1.42 0.93 Susc. Het. 9 34
C230096C10Rik 1436709_at 4 138.9 1.43 1.38 Susc. Hom. 4 141
Creg1 1415947_at 1 167.7 1.46 2.02 Susc. Hom. 1 160
Asah3l 1451355_at 4 86.5 1.51 0.33 Susc. Hom. 13 1
Rdh11 1449209_a_at 12 80.3 1.57 3.02 Susc. Het. 6 32
Mtap2 1434194_at 1 66.2 1.81 1.38 Susc. Hom. 10 102
Tslp 1450004_at 18 33.0 2.34 2.02 Susc. Het. 5 138
Il18 1417932_at 9 50.4 2.34 <0.01 Susc. Het. 9 34

Genes that satisfy two conditions: rewired or novel eQTL in tumors compared to normal skin and significant differential expression in papillomas when tumors
from resistant and susceptible mice are compared. ‘Chr.’ and ‘Mb’ indicate gene location; ‘Fold change’ indicates differential expression between resistant and
susceptible; ‘SAM q-value’ indicates differential expression significance; ‘Higher in’ indicates whether the gene was higher in resistant (Resist.) or susceptible
(Susc.) animals; ‘Higher genotype’ indicates whether heterozygous (Het.) or homozygous (Hom.) alleles were associated with higher expression; ‘eQTL chr.’ and
‘eQTL Mb’ indicate peak eQTL linkage.
0 10 20 30 40 50 60
6
5
4
3
2
1
0
centiMorgans (Chr. 14)
Gzme eQTL LOD
Gzme log
2
expression
(
a
)(
b
)
PapillomasSkin Carcinomas
Figure 5 Granzyme E alleles are associated with su sceptibility. (a) Log
2
expression of Gzme in skin, papillomas, and carcinomas. Gzme
mRNA is not detected in normal skin, and its level of expression is highest in papillomas from mice that are relatively resistant to papilloma
development. Papillomas from resistant animals are plotted as blue circles, susceptible animals as red circles. (b) Expression of Gzme in
papillomas and carcinomas is under germline genetic control. LOD plot for Gzme carcinoma eQTL significance on chromosome 14.

Quigley et al. Genome Biology 2011, 12:R5
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eQTL, which acted almost exclusively in cis, 80% of
these novel ‘perturbation eQTL’ acted in trans (44 of 55;
listed in Table S4 in Additional file 1). A recent report
investigating gene expression changes in human cell
lines in response to ionizing radiation demonstrated that
loci associated with response overwhelmingly acted in
trans [25]. It is possible, therefore, that major perturba-
tionsofgeneexpressionasaresultofDNAdamageor
tumor development are controlled indirectly through
the influence of trans-actin g regulatory factors (for
example, transcri ption factors) rather than through
widespread influence on transcription levels of indivi-
dual genes.
Confirmation of tumor eQTL
Of the 912 transcripts with significant eQTL in the dis-
covery carcinoma eQTL data set, 560 were significant in
the confirmation cohort at a 5% FDR. The number of
samples in the confirmation cohort was relatively s mall
( N = 28), and it is possible that more predicted eQTL
would have been confirmed with a more highly powered
study. These eQTL were mostly cis-eQTL and included
the eQTL affecting Gzme and Il18 expression (Figure S5
in Additional file 1; replicati on results listed in Table S1
in Additional file 1).
Discussion
The pas t few years have witnessed an explosion in gen-
ome-wide association studies of cancer susceptibility in
human populations. While these studies have revealed

many new genetic variants that influence cancer risk,
each variant is predicted to have a very small effect on
susceptibility, and most heritable factors influencing risk
remain to be discovered [26]. Some risk is conferred by
rare variants with large effects, such as the BRCA1/
BRCA2 mutations that increase breast cancer suscept-
ibility. Rare variations cannot be detected by genome-
wide association studies, which analyze only common
(typically >5% minor allele frequency) alleles. Epistati c
interactions between common alleles may also contri-
bute to cancer risk. The latter model is supported by
studies of mouse models of cancer susceptibility, which
have demonstrated that common alleles interact in a
complex fashion to influence risk [27]. However, even in
mouse models that combine defined inbred strains with
dramatically different tumor susceptibilities under well-
controlled environmental conditions, classical mapping
studies have not identified the majority of the risk fac-
tors [28].
The realization that cancer susceptibility is an emer-
gent property of the combinatorial effects of many genes
necessitated the development of more complex network-
based approaches that integrate classical genetics with
gene expression analysis in normal and transformed
tissues. We have previously used a systems genetics
approach to analyze how gene expression networks in
normal whole skin vary between animals that are suscep-
tible or resistant to skin papilloma development. This
approach led to identification of pathways controlling
mitosis, inflammation and tissue remodeling in normal

skin that affect individual susceptibility [8]. In the present
study we have focused on analysis of the rewiring of these
normal gene expression networks during development of
benign and malignant tumors from the same heteroge-
neous population of inter-specific backcross mice.
Our data illuminate the dynamic chan ges in cell popu-
lations, both tumor-derived and host-derived, that
accompany the evolution of solid tumors. Genomic net-
works in squamous cell carcinomas are profoundly
deregulated compared to normal epithelium and benign
papillomas, reflecting major changes in gross tissue orga-
nization and signaling. Allel ic variation continues to
influence tumor gene expression, although this influence
is reduced by the somatic alterations accomp anying pro-
gression. The strongest reduction in tumors is seen in
eQTL tha t act in trans, possibly due to genomic instabil-
ity leading to alterations in transcrip tion factor-mediat ed
control of gene expression and the tissue-specific nature
of trans-eQTL. eQTL under the control of cis-acting ele-
ments in general have stronger effects than trans-eQTL,
and they may be more robust in the face of somatic
genetic changes because the causal variant affects the
gene directly. A recent study compared eQTL detected in
hematopoietic cells at four stages of differentiation and
demonstrated that many eQTL are unique to each state,
and trans-eQTL are less likely to be conserved between
differentiation states than cis-eQTL [29]. Trans-eQTL
were detected in all four states.
We have also identified ‘perturbation eQTL’,which
measure the degree to which changes i n levels of gene

expression between normal and transformed states are
under genetic control. These eQTL reflect genetic con-
trol of the changes that occur in response to exogen ous
damage. In contrast to the steady state eQTL that are
mainly cis-acting, perturbation eQTL act primarily in
trans, similar to a scenario recently described for human
lymphoblast oid cells subjected to ionizing radiation [25].
The mechanistic basis for these observations remain to
be determined by isolation and analysis of the trans-act-
ing factors responsible for these effects.
Genetic and gene expression analyses of tumors
reveals features that cannot be detected by analysis of
normal tissues, such as the cis-eQTL controlling expres-
sion of Il18, Gzme, Map2k4 ,andSpry2 in tumors but
not normal skin. Il18 has an important and complex
role in inflammatory and immune responses; it has been
reported to have both tumor-promoting and anti-tumor
activities in different contexts [30]. It remains to be
Quigley et al. Genome Biology 2011, 12:R5
/>Page 8 of 11
determined whether the gain of germli ne influence over
Il18 expression reflects a change in cell populations or a
modification in cell-autonomous signaling. The presence
of a tumor-specific eQTL for Il18 may reflect differences
in the relative proportions of epithelial and inflamma-
tory cells in the tumors, or may be due to rewiring o f
Il18 signaling during progression.
Unlike Il18, Gzme expression is not detectable in nor-
mal skin, and appears in papillomas and carcinomas
concomitantly with the influx of innate immune cells.

Mice with higher levels of Gzme within their papilloma s
were relatively resistant to papilloma development, in
agreement with a prot ective role for Gzme, and possibly
other granzymes within this gene cluster, in tumor
development. In contrast, mice with high levels of Il18
in their papillomas were m ost susceptible to tumor
development. These data suggest that innate immune
cell responses against tumors are stronger in animals
that carry the SPRET/Ei allele at the Gzme locus, due to
a polymorphism resulting in higher Gzme expression.
This analysis also suggests opposing roles in tum or sus-
ceptibility for Map2k4 and Spry2, genes that exert oppo-
site effects on mitogen-activated protein kinase (MAPK)
signaling.
Tumor signaling can be rewired due to oncogeni c
mutations or loss of tumor suppressor genes, possibly
revealing activity of a germline polymorphism that is
not evident in normal tissue. The identification of sus-
ceptibility genes by a combination of genetic and gene
expression analysis of tumors highlights the power of
this approach to elucidate the genetic architecture of
cancer susceptibility. A combination of genetic and gene
expression analysis of human tumors will complement
genetic association methods and may identify additional
susceptibility factors that cannot be detected using clas-
sical methods.
Materials and methods
Mouse models, gene expression, and aCGH
FVBBX mice were generated and treated with DMBA/
TPA as d escribed in [8]. Gene expression was measured

with the Af fymetrix Mouse Genome 430 2.0 microarray,
Affymetrix annotation release 30. Microarray probesets
where all 11 probes did not hybridize to an annotated
Refseq gene were eliminated fr om analys is. Animals sen-
sitive to papilloma tumorigenesis were defined as >7
papillomas after 20 weeks of treatment (N = 22), resistant
as <2 papillomas at that time point (N = 11). The confir-
mation cohort of FVBBX mice was generated and treated
by the same protocol, with genotypes a nd gene expres-
sion measured as described above. Genomic amplifica-
tion/deletion was measured with a 2,504 probe aCGH
system using log
2
± 0.3 cutoffs for amplification/deletion
[31]. Percentage of the geno me altered was calculated by
dividing each chromosome into 1,000,000 equally spaced
bin s and calling each bin amplified or deleted depending
on the status of the most physically proximal probe for
which a measurement was available. Statistical analysis
was performed with the R package [32].
Statistical analysis of gene expression
Permutation analysis of hierarchical clustering was per-
formed by firs t calculating the distance matrix for sam-
ple gene expression using all presen t genes, counting
cases where the closest papilloma to a given sample was
from the same mouse (N
observed
). We performed 10,000
permutations of the sample l abels and calculated N
perm

in the same manner, reporting the number of t imes
N
perm
≥ N
obs erved
divided by 10,000. Differential expres-
sion was analy zed with the Significan ce Analysis of
Microarrays algorithm [33]. Correlation was defined as
significant at the 5% alpha level using the experiment-
wise genome-wide error rate permutation method as
described in [34]. To calculate the tumor progression
network, skin and carcinoma microarrays were normal-
ized together and genotype-matched skin expression
was subtracted from tumor expression. Mean fold-
change values were approximately normally distributed.
Highly significant change for progression networks was
def ined as >2 standard deviations from the global mea n
change (N = 926). Significant correlation in fold-change
was assessed at the 5% genome-wide level using the
genome-wide error rate method as described in [34]. All
significantly correlated pairs of probes with highly signif-
icant fold-change in expression and membershi p in a
network clique of size 3 or greater were plotted. Corre-
lation networks were drawn using Cytoscape [35].
Microarray results have been deposited in the Gene
Expression Omnibus under accession number [GEO:
GSE21264].
eQTL analysis
Pairs of papillomas from the same animal were com-
bined for eQTL analysis using the mean expression for

each probeset. eQTL were identified by linear regression
as previously described [8]. Briefly, corrected eQTL
P-v alues were calculated by storing the lowest observed
P-value p
minimal-obs
across all 230 SNPs and generating
1,000 shuffled genotypes, calculating p
minimal-perm
for
each permutation, and reporting the rank of p
minimal-obs
in the sorted set of p
minimal-perm
divided by 1,000. The
distribution of corrected P-values was used as input to
Storey’ s QVALUE software [36] to calculate FDR
q-values. Candidate ci s-eQTL were defined as loci
within 30 Mb of the gene they were predicted to affect
(qualitatively similar results were obtained with windows
of 20 and 40 Mb). Interval mapping was performe d
with R/QTL [37]. The 912 carcinoma eQTL from the
Quigley et al. Genome Biology 2011, 12:R5
/>Page 9 of 11
discover y cohort were tested in the confirmatio n dataset
by linear regression of the loci and gene expression
values. The distribution of 912 confirmation P-values
was used with QVALUE to calculate q-values for confir-
mation results.
Additional material
Additional file 1: Additional figures and tables. A schematic overview

of the experiment, additional detailed figures supporting the eQTL
analysis, a table listing eQTL detected in carcinomas, a table detailing cis-
and trans-eQTL counts, a table listing genes altered more than two
standard deviations from the mean in carcinomas compared to matched
normal skin, and a table listing perturbation eQTL identified.
Abbreviations
aCGH: array comparative genomic hybridization; DMBA: dimethyl
benzanthracene; eQTL: expression quantitative trait locus/loci; FDR: false
discovery rate; FVBBX: [SPRET/Ei X FVB/N] X FVB/N; IL: interleukin; TPA:
tetradecanoyl-phorbol acetate.
Acknowledgements
This work was supported by the National Cancer Institute. AB acknowledges
support from the Barbara Bass Bakar Chair of Cancer Genetics. MDT was
supported in part by a Sandler Foundation postdoctoral research fellowship.
JS was supported by the Swedish Research Council and the Tegger
Foundation. KKL was supported by an NIH Kirschstein-NRSA postdoctoral
research fellowship. JPL is partially supported by Carlos III (FIS)/FEDER,
MICIIN/plan-E 2009, JCyL (’Biomedicina y Educación’) and CSIC. The funders
had no role in study design, data collection and analysis, decision to publish,
or preparation of the manuscript. We thank H Quigley, MH Barcellos-Hoff, RJ
Akhurst and members of the Balmain lab for critical reading of the
manuscript, K Jen for assistance with histology, and JH Mao and H Nagase
for discussions in the early stages of this work.
Author details
1
Helen Diller Family Comprehensive Cancer Center, University of California
San Francisco, 1450 Third St, San Francisco, CA 94158, USA.
2
Thoracic
Oncology Program, Department of Surgery, University of California San

Francisco, 1600 Divisadero St, San Francisco, CA 94143, USA.
3
Department of
Laboratory Medicine, University of California San Francisco, 1521 Parnassus
Ave, Room C255, Box 0451, San Francisco, CA 94143, USA.
4
Instituto de
Biología Molecular y Celular del Cáncer, CSIC/Universidad de Salamanca,
Campus M Unamuno s/n, 37007-Salamanca, Spain.
Authors’ contributions
The study was conceived and supervised by AB. Bioinformatics analysis was
performed by DAQ. RNA and DNA extraction was performed by IK, KKL, and
MDT. JPL contributed mice and tumor samples and KKL performed array
analysis for the confirmation study. Taqman assays were performed by JS.
Array CGH data were provided by DGA. The paper was written by DAQ and
AB.
Received: 16 October 2010 Revised: 2 December 2010
Accepted: 18 January 2011 Published: 18 January 2011
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doi:10.1186/gb-2011-12-1-r5
Cite this article as: Quigley et al.: Network analysis of skin tumor

progression identifies a rewired genetic architecture affecting
inflammation and tumor susceptibility. Genome Biology 2011 12:R5.
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